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Item Rapid Discharge of Solid-State Hydrogen Storage Using Porous Silicon and Metal Foam(World Academy of Science, Engineering and Technology, 2022-01-11) Potter, Loralee P.; Schubert, Peter J.; Engineering Technology, School of Engineering and TechnologySolid-state hydrogen storage using catalytically-modified porous silicon can be rapidly charged at moderate pressures (8 bar) without exothermic runaway. Discharge requires temperatures of approximately 110oC, so for larger storage vessels a means is required for thermal energy to penetrate bulk storage media. This can be realized with low-density metal foams, such as Celmet™. This study explores several material and dimensional choices of the metal foam to produce rapid heating of bulk silicon particulates. Experiments run under vacuum and in a pressurized hydrogen environment bracket conditions of empty and full hydrogen storage vessels, respectively. Curve-fitting of the heating profiles at various distances from an external heat source is used to derive both a time delay and a characteristic time constant. System performance metrics of a hydrogen storage subsystem are derived from the experimental results. A techno-economic analysis of the silicon and metal foam provides comparison with other methods of storing hydrogen for mobile and portable applications.Item Attention Mechanism with BERT for Content Annotation and Categorization of Pregnancy-Related Questions on a Community Q&A Site(IEEE, 2020-12) Luo, Xiao; Ding, Haoran; Tang, Matthew; Gandhi, Priyanka; Zhang, Zhan; He, Zhe; Engineering Technology, School of Engineering and TechnologyIn recent years, the social web has been increasingly used for health information seeking, sharing, and subsequent health-related research. Women often use the Internet or social networking sites to seek information related to pregnancy in different stages. They may ask questions about birth control, trying to conceive, labor, or taking care of a newborn or baby. Classifying different types of questions about pregnancy information (e.g., before, during, and after pregnancy) can inform the design of social media and professional websites for pregnancy education and support. This research aims to investigate the attention mechanism built-in or added on top of the BERT model in classifying and annotating the pregnancy-related questions posted on a community Q&A site. We evaluated two BERT-based models and compared them against the traditional machine learning models for question classification. Most importantly, we investigated two attention mechanisms: the built-in self-attention mechanism of BERT and the additional attention layer on top of BERT for relevant term annotation. The classification performance showed that the BERT-based models worked better than the traditional models, and BERT with an additional attention layer can achieve higher overall precision than the basic BERT model. The results also showed that both attention mechanisms work differently on annotating relevant content, and they could serve as feature selection methods for text mining in general.Item Neural networks for mining the associations between diseases and symptoms in clinical notes(Springer, 2018-11-28) Shah, Setu; Luo, Xiao; Kanakasabai, Saravanan; Tuason, Ricardo; Klopper, Gregory; Engineering Technology, School of Engineering and TechnologyThere are challenges for analyzing the narrative clinical notes in Electronic Health Records (EHRs) because of their unstructured nature. Mining the associations between the clinical concepts within the clinical notes can support physicians in making decisions, and provide researchers evidence about disease development and treatment. In this paper, in order to model and analyze disease and symptom relationships in the clinical notes, we present a concept association mining framework that is based on word embedding learned through neural networks. The approach is tested using 154,738 clinical notes from 500 patients, which are extracted from the Indiana University Health’s Electronic Health Records system. All patients are diagnosed with more than one type of disease. The results show that this concept association mining framework can identify related diseases and symptoms. We also propose a method to visualize a patients’ diseases and related symptoms in chronological order. This visualization can provide physicians an overview of the medical history of a patient and support decision making. The presented approach can also be expanded to analyze the associations of other clinical concepts, such as social history, family history, medications, etc.Item Mathematical Model and Experimental Design of Nanocomposite Proximity Sensors(IEEE, 2020-08) Moheimani, Reza; Pasharavesh, Abdolreza; Agarwal, Mangilal; Dalir, Hamid; Engineering Technology, School of Engineering and TechnologyA mathematical model of fringe capacitance for a nano-based proximity sensor, which takes the presence of different resistivities into account, is developed. An analytical solution obtained for a rectangular-shape sensor with applying of Gauss, Conversation of Charge and Ohm laws into Laplace's equation ∇2V (x, y, z, t) = 0 gives the electric potential distribution by which the fringe capacitance in a 2D domain area can be calculated. The calculated capacitance evidently decreases drastically due to the fringe phenomena while object moves toward the polymeric sensor. The model also asserts that the change of capacitance is under a noticeable influence of sensor resistivity, particularly in the range of 103-105Ω.m, the initial capacitance varies from 0.045pF to 0.024 pF. The fabricated flexible nanocomposite sensors, Thermoplastic Polyurethane (TPU) reinforced by 1wt.% Carbon Nanotubes (CNTs) having resistivity 105Ω.m, are capable of detecting presence of an external object in a wide range of distance and indicating remarkable correlation with the mathematical solution. Our proximity sensor fabrication is straightforward and relatively simple. An unprecedented detection range of measurement reveals promising ability of this proximity sensor in applications of motion analysis and healthcare systems.Item Fuzzy Controller Algorithm for Automated HVAC Control(IAARC, 2020-10) Chae, Myungjin; Kang, Kyubyung; Koo, Dan D.; Oh, Sukjoon; Chun, Jae Youl; Engineering Technology, School of Engineering and TechnologyThis research presents the design framework of the artificial intelligent algorithm for an automated building management system. The AI system uses wireless sensor data or IoT (Internet of Things) and user's feedback together. The wireless sensors collect data such as temperature (indoor and outdoor), humidity, light, user occupancy of the facility, and Volatile Organic Compounds (VOC) which is known as the source of the Sick Building Syndrome (SBS) or New Building Syndrome because VOC are often found in new buildings or old buildings with new interior improvement and they can be controlled and reduced by appropriate ventilation efforts. The collected data using wireless sensors are post-processed to be used in the neural network, which is trained in accordance with the collected data pattern. When the users of the facility have the control of the building's ventilation system and the AI system is fully trained using the user input, it will mimic the user's pattern and control the building system automatically just as the user wants. In this research, data were collected from 4 different buildings: university library, university cafeteria, a local coffee shop, and a residential house. Fuzzy logic controller is also developed for better performance of the HVAC. Indoor air quality, temperature (indoor and outdoor), HVAC fan speed and heater power are used for fuzzified output. As a result, the framework and simulation model for the energy efficient AI controller has been developed using fuzzy logic controller and the neural network-based energy usage prediction model.Item Design and Evaluate the Factors for Flipped Classrooms for Data Management Courses(Institute of Electrical and Electronics Engineers, 2020-10) Mithun, Shamima; Luo, Xiao; Engineering Technology, School of Engineering and TechnologyThis Research to Practice Full Paper presents a framework to evaluate and design flipped classroom activities for data science and management courses. Variants of flipped classrooms have been employed in STEM fields with great success in students' learning outcomes. Research shows that flipped classrooms would improve students' learning if it is implemented following rigorous procedures of an efficient instructional design. As a result, one of the critical focus of current flipped classroom research is what factors educators need to consider when designing a flipped learning environment. Currently, educators incorporate various factors such as "pre-recorded video lecture", "group activity" as a trial and error basis and adjust these factors based on their own experience and students' feedback. On the other hand, the emergence of big data expects a new graduate to demonstrate mastery of concepts and skills for data acquisition, management, and analysis of inference from data when they enter the workforce. Currently, there is no systematic approach available to design a flipped classroom that is for the data science and management courses. In this research, we develop a framework first to investigate and evaluate the flipped classroom factors mentioned in the literature and identify a few that are most relevant to the two data management courses at our institute. Then, we classify each course topics into broader categories. So that the flipped classroom model can be developed for each category. For the flipped classroom for each category, we identify the pre-class and in-class activities to meet a certain learning objective for that topic category for each course. To evaluate the effectiveness of different factors as well as our flipped classroom models, students' performance data, interviews, and surveys are conducted. This process is transformative and can be employed by other STEM disciplines to find the most influential factors to design effective flipped learning classrooms.Item Measurement of Wastewater Discharge in Sewer Pipes Using Image Analysis(MDPI, 2020-06) Ji, Hyon Wook; Yoo, Sung Soo; Lee, Bong-Jae; Koo, Dan Daehyun; Kang, Jeong-Hee; Engineering Technology, School of Engineering and TechnologyGenerally, the amount of wastewater in sewerage pipes is measured using sensor-based devices such as submerged area velocity flow meters or non-contact flow meters. However, these flow meters do not provide accurate measurements because of impurities, corrosion, and measurement instability due to high turbidity. However, cameras have advantages such as their low cost, easy service, and convenient operation compared to the sensors. Therefore, in this study, we examined the following three methods for measuring the flow rate by capturing images inside of a sewer pipe using a camera and analyzing the images to calculate the water level: direct visual inspection and recording, image processing, and deep learning. The MATLAB image processing toolbox was used for analysis. The image processing found the boundary line by adjusting the contrast of the image or removing noise; a network to find the boundary line between wastewater and sewer pipe was created after training the image segmentation results and placing them into three categories using deep learning. From the recognized water levels, geometrical features were used to identify the boundary lines, and flow velocities and flow rates were calculated from Manning’s equation. Using direct inspection and image-processing techniques, boundary lines in images were detected at rates of 12% and 53%, respectively. Although the deep-learning model required training, it demonstrated 100% water-level detection, thereby proving to be the most advantageous method. Moreover, there is enough potential to increase the accuracy of deep learning, and it can be a possible replacement for existing flow measurement sensors.Item Analysis of the Flow Performance of the Complex Cross-Section Module to Reduce the Sedimentation in a Combined Sewer Pipe(MDPI, 2020-11) Ji, Hyon Wook; Yoo, Sung Soo; Koo, Dan Daehyun; Kang, Jeong-Hee; Engineering Technology, School of Engineering and TechnologyThe difference in the amount of stormwater and sewage in a combined sewer system is significantly large in areas where heavy rainfall is concentrated. This leads to a low water level and slow flow velocity inside the pipes, which causes sedimentation and odor on non-rainy days. A complex cross-section module increases the flow velocity by creating a small waterway inside the pipe for sewage to flow on non-rainy days. While considering Manning’s equation, we applied the principle where the flow velocity is proportional to two-thirds of the power of the hydraulic radius. The flow velocity of a circular pipe with a diameter of 450 mm and the corresponding complex cross-section module was analyzed by applying Manning’s equation and numerical modeling to show the effects of the complex cross-section module. The tractive force was compared based on a lab-scale experiment. When all conditions were identical except for the cross-sectional shape, the average flow velocity of the complex cross-section module was 14% higher while the size of the transported sand grains was up to 0.5 mm larger. This increase in flow velocity can be even higher if the roughness coefficient of aging pipes can be decreased.Item Rule-Based Scan-to-BIM Mapping Pipeline in the Plumbing System(MDPI, 2020-11) Kang, Taewook; Patil, Shashidhar; Kang, Kyubyung; Koo, Dan; Kim, Jonghoon; Engineering Technology, School of Engineering and TechnologyThe number of scan-to-BIM projects that convert scanned data into Building Information Modeling (BIM) for facility management applications in the Mechanical, Electrical and Plumbing (MEP) fields has been increasing. This conversion features an application purpose-oriented process, so the Scan-to-BIM work parameters to be applied vary in each project. Inevitably, a modeler manually adjusts the BIM modeling parameters according to the application purpose, and repeats the Scan-to-BIM process until the desired result is achieved. This repetitive manual process has adverse consequences for project productivity and quality. If the Scan-to-BIM process can be formalized based on predefined rules, the repetitive process in various cases can be automated by re-adjusting only the parameters. In addition, the predefined rule-based Scan-to-BIM pipeline can be stored and reused as a library. This study proposes a rule-based Scan-to-BIM Mapping Pipeline to support application-oriented Scan-to-BIM process automation, variability and reusability. The application target of the proposed pipeline method is the plumbing system that occupies a large number of MEPs. The proposed method was implemented using an automatic generation algorithm, and its effectiveness was verified.Item Using ePortfolios to Facilitate Transfer Student Success(ASEE, 2020-06) Cooney, Elaine M.; Freije, Elizabeth; Zhao, Mengyuan (Alice); Engineering Technology, School of Engineering and TechnologyUsing ePortfolios to Facilitate Transfer Student Success Abstract This paper describes the use of an ePortolio to facilitate success as students transfer from a community college system to baccalaureate engineering technology programs as juniors. The ePortfolio is created as part of a transfer seminar course that meets just before and during their first semester at university. The course has three purposes: 1. Orient to the university 2. Synthesize learning from Associate of Science (AS) 3. Identify and complete any prerequisite knowledge for junior level courses. Some material may be included in the university freshman and sophomore course, but not included in associate of science courses at community college. The creation of an ePortfolio during the transfer seminar assists with the synthesis of previous learning and filling in any gaps in knowledge needed for rest of the BS plan of study. To guide the artifact selection for the ePortfolio, university faculty reviewed the state-wide core competencies and compared them to the pre-requisite knowledge required for junior level courses. The most important competencies were targeted for use in the ePortfolio. During the seminar class, students identify artifacts from their AS classes that demonstrate the competency, upload an electronic representation of the work, and write a reflection about how the artifact demonstrates their competence. The reflections are assessed by the faculty using rubrics published in the course management system. The ePortfolio tool is part of the CourseNetworking (CN) platform. CN has many advantages as an ePortfolio for this application, but the most important is that CN lets individual users own their ePortfolio for their lifetime; the site is not owned by the college or the university. Even after graduation or transferring to a new school, users may continue to access and maintain their CN ePortfolio, free of charge. This enables community college students to begin their artifact collection while taking their associate degree classes, and then complete their reflections after they transfer to the university. The use of ePortfolios and reflection on learning is an effective way to give students confidence as they begin a new program and to bridge any gaps in prerequisite knowledge.